Design and Development of a Machine Learning Tool for an Innovation-Based Learning MOOC

Massive Open Online Courses (MOOCs) are allowing education to become accessible to all learners. HoNs ever, computers are currently not able to provide the same individual expertise and support as a human instructor, making it difficult to tailor the course for each student. This work proposes using machine learning analytics to provide useful individualized feedback to learners in a Cardiovascular Engineering Innovation-Based Learning course. The system will monitor students' habits as they form learning objectives and progress through the course, allowing the system to learn to make recommendations for the student. The architecture of the developed system is presented, a test set is used to verify, and the context of future pilot tests is introduced. Although this work is presently specific to one particular course, it can be adapted to allow for personalization of any MOOC that allows for differentiated, higher level learning.